Computational Identification and Characterization of Potential T-Cell Epitope for the Utility of Vaccine Design Against Enterotoxigenic Escherichia coli

  • Fariya Khan
  • Vivek Srivastava
  • Ajay KumarEmail author


Diarrhea is considered to be a leading cause of death in children less than 5 years of age and the major cause of diarrheal diseases in infants and young children as well as in individuals traveling to endemic areas are caused by Enterotoxigenic Escherichia coli (ETEC) bacteria. Despite the severe outbreaks of diarrheal infections, increasing resistance to antibiotics and slow experimental analysis, there is a lack of an effective vaccine. T-cell epitope mapping is a desirable method in predicting the antigenic proteins and epitope-based vaccines have remarkable privilege over the conventional ones since they are specific, able to avoid undesirable immune responses, generate long lasting immunity, and are reasonably cheaper. In the present study, prediction and modeling of the T cell epitopes of enterotoxigenic Escherichia coli antigenic proteins along with the binding simulation studies of the highest binding scorers with their corresponding MHC class II alleles were performed. Here, an immunoinformatic tool ProPred was used to predict the promiscuous MHC class II restricted T-cell epitopes of the bacterial antigenic proteins. Further, different computational tools were used to analyze the physiochemical properties of the most immunogenic protein that can be manipulated as a potential candidate in the development of vaccine. Docking of the predicted epitopes was tested to detect the binding efficiency with MHC II alleles. After rigorous investigations, the 3 epitopes- YKFVPWFNL, LNIDITGCA and MKASNGLNI were found to be the most potential T cell epitopes. Therefore, peptides predicted through various computational approaches may have the potential to trigger an effective T cell-mediated immune response and can be suggested for experimental analysis to validate their antigenic properties as a suitable vaccine candidate.


Epitope Docking Immunoinformatic MHC Peptides 



The authors gratefully acknowledge the necessary computational facilities and constant supervision provided by Department of Biotechnology, Faculty of Engineering and Technology, Rama University Uttar Pradesh Kanpur, India for their generous support during the research work.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10989_2018_9671_MOESM1_ESM.doc (390 kb)
Supplementary material 1 (DOC 390 KB)


  1. Ansari HR, Flower DR, Raghava GPS (2009) AntigenDB: an immunoinformatics database of pathogen antigens. Nucleic Acids Res 38:847–853CrossRefGoogle Scholar
  2. Bourgeois AL, Wierzba TF, Walker RI (2016) Status of vaccine research and development for enterotoxigenic Escherichia coli. Vaccine 34:2880–2886CrossRefGoogle Scholar
  3. Castellino F, Zhong G, Germain RN (1997) Antigen presentation by MHC class II molecules: invariant chain function, protein trafficking, and the molecular basis of diverse determinant capture. Hum Immunol 54:59–69CrossRefGoogle Scholar
  4. Chen F, Mackey AJ, Stoeckert CJ Jr., Roos DS (2006) Ortho MCL-DB: Querying a comprehensive multi-species collection of ortholog groups. Nucleic Acids Res 34:363–368CrossRefGoogle Scholar
  5. Dimitrov I, Naneva L, Doytchinova I, Bangov I (2014) AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics 30:846–851CrossRefGoogle Scholar
  6. Doytchinova IA, Darren R, Flower (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8:4CrossRefGoogle Scholar
  7. Fleckenstein JM, Hardwidge PR, Munson GP, Rasko DA, Sommerfelt H, Steinsland H (2010) Molecular mechanisms of enterotoxigenic Escherichia coli infection. Microbes Infect 12:89–98CrossRefGoogle Scholar
  8. Fleckenstein JM, Sheikh A, Qadri F (2014) Novel antigens for enterotoxigenic Escherichia coli (ETEC)vaccines. Expert Rev vaccines 13:631–639CrossRefGoogle Scholar
  9. Guan P, Doytchinova IA, Zygouri C, Flower DR (2003) MHC Pred: bringing a quantitative dimension to the online prediction of MHC binding. Appl Bioinformatics 2:63–66Google Scholar
  10. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R (2013) In silico approach for predicting toxicity of peptides and proteins. PLoS ONE 8:e73957 . CrossRefGoogle Scholar
  11. Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecular dynamics. J Mol Graphics 14:33–38CrossRefGoogle Scholar
  12. Isidean SD, Riddle MS, Savarino SJ, Porter CK (2011) A systematic review of ETEC epidemiology focusingon colonization factor and toxin expression. Vaccine 29:6167–6178CrossRefGoogle Scholar
  13. James CP, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802CrossRefGoogle Scholar
  14. Jiang ZD, Lowe B, Verenkar MP, Ashley D, Steffen R, Tornieporth N et al (2002) Prevalence of enteric pathogens among international travelers with diarrhea acquired in Kenya (Mombasa), India (Goa), or Jamaica (Montego Bay). J Infect Dis 185:497–502CrossRefGoogle Scholar
  15. Kamthania M, Sharma DK (2015) Screening and structure-based modeling of T-cell epitopes of Nipah virus proteome: an immunoinformatic approach for designing peptide-based vaccine. 3 Biotech 5:877–882CrossRefGoogle Scholar
  16. Kaur H, Garg A, Raghava GPS(2007) PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. Protein Pept Lett 14:626–630CrossRefGoogle Scholar
  17. Khan F, Srivastava V, Kumar A (2017) Epitope based peptide prediction from proteome of enteerotoxigenic E. coli. Int J Peptide Res Ther. Google Scholar
  18. Kolaskar AS, Tongaonkar PC (1990) A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 276:172–174CrossRefGoogle Scholar
  19. Krogh A, Larsson B, Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580CrossRefGoogle Scholar
  20. Kumar SG, Subitha L (2012) Diarrhoeal diseases in developing countries: a situational analysis. Kathmandu Univ Med J (KUMJ) 10:83–88CrossRefGoogle Scholar
  21. Kumar A, Hays M, Lim F, Foster LJ, Zhou M, Zhu G, Miesner T, Hardwidge PR (2015) Protective enterotoxigenic Escherichia coli antigens in a Murine Intranasal Challenge Model. PLoS. Google Scholar
  22. Lawrence JS, Calvo-Calle M (2009) HLA-DR: molecular insights and vaccine design. Curr Pharm Des 15:3249–3261CrossRefGoogle Scholar
  23. Levine MM, Caplan ES, Waterman D, Cash RA, Hornick RB, Snyder MJ (1977) Diarrhea caused by Escherichia coli that produce only heat-stable enterotoxin. Infect Immun 17:78–82Google Scholar
  24. Lombard M, Pastoret PP, Moulin AM (2007) A brief history of vaccines and vaccination. Rev Sci Tech 26:29–48CrossRefGoogle Scholar
  25. Lovell SC, Davis IW, Arendall WB, de Bakker PI, Word JM, Prisant MG, Richardson JS, DC. Richardson (2003) Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins 50:437–450CrossRefGoogle Scholar
  26. Manzalawy YE, Honavar V (2010) A framework for developing epitope prediction tools. In: Proceedings of the first ACM international conference on bioinformatics and computational biology, ACM, pp 660–662Google Scholar
  27. Morris GM, Goodsell DS, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithmand an empirical binding free energy function. J Comput Chem 4:1639–1662CrossRefGoogle Scholar
  28. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 andAutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 16:2785–2791CrossRefGoogle Scholar
  29. PATH (2011) BIO Ventures for Global Health, The case for investment in enterotoxigenic Escherichia coli vaccines.
  30. Porter CK, Riddle MS, Alcala AN, Sack DA, Harro C, Chakraborty S et al (2016) An evidenced-based scale of disease severity following human challenge with enteroxigenic Escherichia coli. PLoS ONE. Google Scholar
  31. Rasko DA, Rosovitz MJ, Myers GSA, Mongodin EF, Fricke WF, Gajer P, Crabtree J, Sebaihia M, Thomson NR, Chaudhuri R, Henderson. IR, Sperandio V (2008) The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates. J Bacteriol 190:6881–6893CrossRefGoogle Scholar
  32. Riddle SM Connor BA, DuPont HL (2016) ACG clinical guideline: diagnosis, treatment, and prevention of acute diarrheal infections in adults. Am J Gastroenterol 111:602–622CrossRefGoogle Scholar
  33. Robinson J, Halliwell JA, Hayhurst JH, Flicek P, Parham P, Marsh SGE (1995) The IPD and IMGT/HLA Database: allele variant databases. Nucleic Acids Res 43:423–431CrossRefGoogle Scholar
  34. Roy K, Bartels S, Qadri F, Fleckenstein JM (2010) Enterotoxigenic Escherichia coli elicits immune responses to multiple surface proteins. Infect Immun 78:3027–3035CrossRefGoogle Scholar
  35. Sachdeva G, Kumar K, Jain P, Ramachandran S (2005) SPAAN: A software program for prediction of adhesins and adhesin-like proteins using neural networks. Bioinformatics 21:483–491CrossRefGoogle Scholar
  36. Sali A, Potterton L, Feng Y, Herman V, Martin K (1995) Evaluation of comparative protein modeling by Modeller. Protein 23:318–326CrossRefGoogle Scholar
  37. Singh H, Raghava GPS (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17:1236–1237CrossRefGoogle Scholar
  38. Smith HW, Halls S (1967) Observations by the ligated intestinal segment and oral inoculation methods on Escherichia coli infections in pigs, calves, lambs and rabbits. J Pathol Bacteriol 93:499–529CrossRefGoogle Scholar
  39. Tang H, Liu XS, Fang YZ, Pan L, Zhang ZW et al (2012) The epitopes of foot and mouth disease. Asian J Anim Vet Adv 7:1261–1265CrossRefGoogle Scholar
  40. UNICEF (2016) One is too many: ending child death from pneumonia and diarrhea.
  41. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD et al (1999) Protein identification and analysis tools in the ExPASy server. Methods Mol Biol 112:531–552Google Scholar
  42. Yongqun ZX, Mobley HL (2010) Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J Biomed Biotechnol. Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biotechnology, Faculty of Engineering & TechnologyRama UniversityKanpurIndia

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